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Rising technologies as wireless technologies, Sensing technologies and Communication technologies transformed the Internet into an integrated network of things termed as Internet-of-Things (IoT). IoT is a network of things connected over by Internet, all the pros and cons of internet are also inherited to IoT applications. Hence, security and privacy of user data is a serious issue in IoT. In this...
Biometrics based authentication systems face the major challenge in securing the template. Although multi-modal biometric techniques and cryptographic techniques offer security but they can be easily compromised. This paper explores the possibility of combining features from the same biometric trait (fingerprints) i.e. keeping it unimodal but still offering the same security as that of the combined...
The blockchain has fueled one of the most enthusiastic bursts of activity in applied cryptography in years, but outstanding problems in security and privacy research must be solved for blockchain technologies to go beyond the hype and reach their full potential. At the first IEEE Privacy and Security on the Blockchain Workshop (IEEE S&B), we presented peer-reviewed papers bringing together academia...
Differential Privacy (DP) has received increasing attention as a rigorous privacy framework. Many existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primitives, which assume that the data are independent, or that adversaries do not have knowledge of the data correlations. However, continuous generated data in the real world tend to be temporally correlated, and such...
Having an effective security level for Embedded System (ES), helps a reliable and stable operation of this system. In order to identify, if the current security level for a given ES is effective or not, we need a proactive evaluation for this security level. The evaluation of the security level for ESs is not straightforward process, things like the heterogeneity among the components of ES complicate...
Data is becoming increasingly personal. Individuals regularly interact with a wide variety of structured data, from SQLite databases on phones, to HR spreadsheets, to personal sensors, to open government data appearing in news articles. Although these workloads are important, many of the classical challenges associated with scale and Big Data do not apply. This panel brings together experts in a variety...
Preserving privacy of continuous and/or high-dimensional data such as images, videos and audios is challenging. Syntactic anonymization methods were proposed typically for discrete data types and can be unsuitable. Differential privacy, which provides a stricter type of privacy, has shown more success in sanitizing continuous data. However, both syntactic and differential privacy are susceptible to...
Enterprises and governments around the world have been attempting to leverage intelligence from the community by making formally in-house database available to the public for analyzing. The released data was often "anonymized": sensitive attributes were removed from the dataset for privacy protection. However it is proved that masking sensitive attributes alone is not adequate for data protection...
One way to make more spectrum available is through white space sharing, where secondary spectrum users are allowed to transmit from any location except inside exclusion zones (EZs) that are drawn around primary spectrum users (PUs). However, EZ boundaries reveal the locations of PUs, and for some devices, location privacy is extremely important. Location privacy can be improved by increasing the area...
In this paper, we propose a similarity searchable encryption in the symmetric key setting for Euclidean distance, by extending the functional encryption scheme for inner product proposed by Bishop et al. [2]. Our scheme performs predetermined encoding independently of vectors x and y, and it obtains the Euclidean distance between the two vectors while they remain encrypted.
Availability of information in profusion in the internet and databases is common knowledge. It has to be viewed in the backdrop of chances for disclosure of such information by a third party. Privacy Preserving Data Mining (PPDM) is in use for maintaining the privacy of individuals. Numerous updated methods are available for the purpose. Evolutionary Algorithms (EA's) are able to provide effective...
Nowadays, various network services, such as online shops and reservation of facilities, have been used with the spread of the Internet. Some of these services request to offer personal information to users. However, we cannot know how offered personal information is used. Thus, we feel uneasy to offer personal information to service providers. For this reason, we proposed a framework that a user can...
Nowadays, data has become more important as the core resource for the information society. However, with the development of data analysis techniques, the privacy violation such as leakage of sensitive data and personal identification exposure are also increasing. Differential privacy is the technique to satisfy the requirement that any additional information should not be disclosed except information...
We present a set of protocols for creating a secure processing tree used for completing a task without divulging critical information. We analyze the information that is exchanged during the protocols and the effect of compromising participants.
Fingerprint template protection has recently drawn much attention due to public security and privacy awareness. In this paper, we demonstrate an approach for designing a non-invertible fingerprint template by constructing the many-to-many bit-string polar transformations with different minutiae points. We explore the relative relationship of minutiae points in polar system, which is registration-free...
In this paper, we introduce new methods for releasing differentially private graphs. Our techniques are based on a new way to distribute noise among edge weights. More precisely, we rely on the addition of noise whose amplitude is edge-calibrated and optimize the distribution of the privacy budget among subsets of edges. The generic privacy framework that we propose can capture most of the privacy...
The growth of the Internet of Things (IoT) creates the possibility of decentralized systems of sensing and actuation, potentially on a global scale. IoT devices connected to cloud networks can offer sensing and actuation as a service enabling networks of sensors to grow to a global scale. But extremely large sensor networks can violate privacy, especially in the case where IoT devices are mobile and...
Although using geolocation databases for spectrum sharing has many pragmatic advantages, it also raises potentially serious operational security (OPSEC) issues. OPSEC is especially a paramount consideration in the light of recent calls in the U.S. for spectrum sharing between federal government (including military) systems and non- government systems (e.g., cellular service providers). In this paper,...
Differential privacy (DP) is a promising tool for preserving privacy during data publication, as it provides strong theoretical privacy guarantees in face of adversaries with arbitrary background knowledge. Histogram, as the result of a set of count queries, serves as a core statistical tool to report data distributions and is in fact viewed as the fundamental method for many other statistical analysis...
The database-driven cognitive radio network (CRN) is regarded as a promising way for a better utilization of spectrum resources without introducing the interference to primary users (PUs). However, there are some critical security and privacy issues in database-driven CRNs, which have been rarely discussed before. First of all, in order to retrieve the spectrum available information (SAI) of one's...
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